Improved impossible differential and biclique cryptanalysis of HIGHT

HIGHT is a lightweight block cipher introduced in CHES 2006 by Hong et al as a block cipher suitable for low‐resource applications. In this paper, we propose improved impossible differential and biclique attacks on HIGHT block cipher both exploiting the permutation‐based property of the cipher's key schedule algorithm as well as its low diffusion. For impossible differential attack, we found a new 17‐round impossible differential characteristic that enables us to propose a new 27‐round impossible differential attack. The total time complexity of the attack is 2^120.4 where an amount of 2^59.3 chosen plaintext‐ciphertext pairs and 2^107.4 memory are required. We also instantiate a new biclique cryptanalysis of HIGHT, which is based on the new idea of splitting each of the forward and backward keys into 2 parts where the computations associated to each one are performed independently. The time complexity and data complexity of this attack are 2^125.7 and 2⁴², respectively. To the best of our knowledge, this is the fastest biclique attack on full‐round HIGHT.     

Phase Transformation Kinetics During Annealing of Cold-Rolled AISI 309Si Stainless Steel

Metallurgical and Materials Transactions A - The effects of cold rolling and the subsequent annealing were studied for the AISI 309Si stainless steel. During annealing of cold-rolled sheets, the...     

Generalized carrier to interference ratio analysis for shotgun cellular systems in multiple dimensions over composite Rayleigh–Lognormal (Suzuki) fading

Telecommunication Systems - This paper analyzes the carrier-to-interference ratio (CIR) of the so-called shotgun cellular systems (SCSs) in $$\tau $$ dimensions ( $$\tau =1, 2,$$ and 3). SCSs are...     

New Approaches for Estimation of Monthly Rainfall Based on GEP-ARCH and ANN-ARCH Hybrid Models

Accurate estimation of rainfall has an important role in the optimal water resources management, as well as hydrological and climatological studies. In the present study, two novel types of hybrid models, namely gene expression programming-autoregressive conditional heteroscedasticity (GEP-ARCH) and artificial neural networks-autoregressive conditional heteroscedasticity (ANN-ARCH) are introduced to estimate monthly rainfall time series. To fulfill this purpose, five stations with various climatic conditions were selected in Iran. The lagged monthly rainfall data was utilized to develop the different GEP and ANN scenarios. The performance of proposed hybrid models was compared to the GEP and ANN models using root mean square error (RMSE) and coefficient of determination (R²). The results show that the proposed GEP-ARCH and ANN-ARCH models give a much better performance than the GEP and ANN in all of the studied stations with various climates. Furthermore, the ANN-ARCH model generally presents better performance in comparison with the GEP-ARCH model.     

Evaluation of hydrogen effect on the fatigue crack growth behavior of medium-Mn steels via in-situ hydrogen plasma charging in an environmental scanning electron microscope

Fatigue crack growth(FCG)tests were conducted on a medium-Mn steel annealed at two intercritical annealing temperatures,resulting in different austenite(γ)to ferrite(α)phase fractions and different γ(meta-)stabilities.Novel in-situ hydrogen plasma charging was combined with in-situ cyclic loading in an environmental scanning electron microscope(ESEM).The in-situ hydrogen plasma charging increased the fatigue crack growth rate(FCGR)by up to two times in comparison with the reference tests in vacuum.Fractographic investigations showed a brittle-like crack growth or boundary cracking manner in the hydrogen environment while a ductile transgranular manner in vacuum.For both materials,the plastic deformation zone showed a reduced size along the hydrogen-influenced fracture path in comparison with that in vacuum.The difference in the hydrogen-assisted FCG of the medium-Mn steel with different microstructures was explained in terms of phase fraction,phase stability,yielding strength and hydrogen distribution.This refined study can help to understand the FCG mechanism without or with hydrogen under in-situ hydrogen charging conditions and can provide some insights from the applications point of view.     

Synthesis of undoped and Fe nanoparticles doped SnO<Subscript>2</Subscript> nanostructure: study of structural,optical and electrocatalytic properties

In this study, undoped and nano Fe doped SnO₂ nanostructures were synthesized by hydrothermal method. Characterization of the synthesized nanostructures was performed by X-ray diffraction and scanning electron microscopy. For investigation of optical properties, photo luminescence and UV–Vis spectrum were taken. The electrochemical response of the carbon paste electrode modified with synthesized nanostructures toward levodopa (L-Dopa) was studied. Cyclic voltammetry studies using prepared modified electrodes showed outstanding electrocatalytic properties towards electro-oxidation of L-Dopa and a significant reduction was observed in anodic over voltage compared to the bare electrode. Obtained results indicated the effective role of the employed dopant. Best response in terms of the current enhancement, overvoltage reduction, and reversibility improvement of the L-Dopa oxidation reaction under experimental conditions was obtained by modified electrode with Fe doped SnO₂ nanoparticles.     

Estimation of homogenisation time for superalloys based on a new diffusional model

The inter-diffusion of segregating elements (Nb, Mo, and Ti) in the cast ingot of Inconel 718 superalloy during homogenisation at elevated temperature was studied by a new model based on the characteristic diffusion length. The proposed simple method was used for obtaining the homogenisation time required for back-diffusion of each element. Therefore, the developed model can be used for both studying the diffusion of alloying elements and estimation of homogenisation time, where the latter is important from the industrial standpoint.     

Incremental one-class classifier based on convex–concave hull

Pattern Analysis and Applications - One subject that has been considered less is a binary classification on data streams with concept drifting in which only information of one class (target class)...     

Wideband Frequency-Reconfigurable Antenna for Airborne Applications

Wireless Personal Communications - One of the important objectives of underwater acoustic sensor network is to extend the lifespan of a network which depends on the topology control mechanisms....     

Partial fingerprint identification for large databases

Nowadays, most fingerprint sensors capture partial fingerprint images. Incomplete, fragmentary, or partial fingerprint identification in large databases is an attractive research topic and is remained as an important and challenging problem. Accordingly, conventional fingerprint identification systems are not capable of providing convincing results. To overcome this problem, we need a fast and accurate identification strategy. In this context, fingerprint indexing is commonly used to speed up the identification process. This paper proposes a robust and fast identification system that combines two indexing algorithms. One of the indexing algorithms uses minutiae triplets, and the other uses orientation field (OF) to index and retrieve fingerprints. Furthermore, the proposal uses some partial fingerprint matching methods on final candidate list obtained from the indexing stage. The proposal is evaluated over two national institutes of standards and technology (NIST) datasets and four fingerprint verification competition (FVC) datasets leading to low identification times with no accuracy loss.